1. Working in different contexts: RStudio Projects
2. Dynamic document generation: RMarkdown
3. Version control: Git + GitHub
4. Environment management: renv
5. Containerization: Docker
6. Start collaborating

• Peikert, A., & Brandmaier, A. M. (2020). A Reproducible Data Analysis Workflow with R Markdown, Git, Make, and Docker. https://doi.org/10.31234/osf.io/8xzqy

• What it does:
  • Allows to work in multiple different contexts (projects), e.g. one for each experiment
  • Each project is own working directory, workspace, history, and source documents
  • Each project is associated with a folder on your computer (= working directory)
• Why it helps:
  • Have a separate, shareable working environment for each experiment
  • Keep all the files associated with a project together — data, scripts, results, figures
  • Work on multiple projects at once, each associated with its packages (and package versions), loaded data, etc.
  • Use only relative paths
  • Necessary basis for version control

• In RStudio: File > New Project > …

• If necessary

• What it does:
  • Creates dynamic documents with embedded chunks of code (R, python, Julia, stan, …), computed results , written text etc. (= LaTex)
  • Markdown-files can be exported to documents (docx, rtf), presentations, pdfs, or websites (html) e.g using the knitr (Xie, 2015, 2020) and tinytex (Xie, 2015, 2020; for pdfs)
  • R code is dynamically rendered, and can be given in separate chunks (’’‘{r}’’‘) or inline (’ r … ’)
• Why it helps:
  • Simple language (\(\neq\) LaTex)
  • Integrates directly with statistical software (R Studio)
  • Saves code and output in one file
  • Reduces copy&paste errors: reported results consistent with actual results

• …

• If necessary

• What it does:
  • …
  • …
• Why it helps:
  • …
  • …

• …

• If necessary

• What it does:
  • Creates a project-specific library of packages in the project folder
  • Overwrites install.packages() to install packages there
  • Keeps track of package versions in the renv.lock file
• Why it helps:
  • Keeps package versions untouched by other projects
  • Allows you to revert to the previous state when an update has broken your analysis
  • Makes it easier to share package versions with your collaborators (e.g., via GitHub)

1. Install the renv package: install.packages(renv)
2. Initialize your project library: renv::init()
3. Install and update your packages as usual: install.packages("pkgname")
4. Save the current state of the project library: renv::snapshot()
5. Revert to a previous state if an update caused problems: renv::restore()

You can skip step #2 if you select “Use renv with this project” during project creation.

• If necessary

• What it does:
  • …
  • …
• Why it helps:
  • …
  • …

• …

• If necessary

• …